Cancers are, in general, classified by the cell type and site of origin, such as lymph node, lung, bone marrow, etc. In addition, they are sub-classified based on histologic characteristics and the proteins expressed by the cancer cells, as determined by immunophenotyping. These sub-types define biologically distinct diseases, each of which has its own distinct clinical behavior and, hence, its own specific therapy. For example, the most common testicular cancer, seminoma, is treated and in most cases cured with radiation, whereas the non-seminomatous testicular cancers do not respond to radiation therapy. Similarly, among blood cancers, lymphoblastic leukemia, the most common malignancy in childhood, can be cured by multi-agent chemotherapy, but multiple myeloma, one of the most common lymphoid cancers in adults, is invariably fatal. Cancer sub-typing, along with the degree of cancer spreading (stage) and the underlying health of the patient, is therefore a critical consideration for choice of therapy. However, although cancer sub-typing contributes to prognostic and therapeutic decisions and statistically significant outcomes can be obtained from studies of large cohorts of patients, considerable variability in disease outcome exists among individuals of stage-matched patients in most cancer subclasses. It is particularly difficult to predict the outcome of pre-malignant lesions.
Cancer cells by definition are monoclonal, arising from inappropriate expansion of one single cell that has lost controls in both cell cycle (proliferation) and apoptosis (cell death). In normal cells, it is well established that cell cycle progression is controlled by the balance between positive and negative cell cycle regulators, and not by a single cell cycle regulator. However, the mechanism that underlies the loss of cell cycle control in each cancer type is not understood. Thus far, cell cycle dysregulation in cancers is often assayed at the DNA level by the analysis of gene deletion and mutation, at the RNA level by RT-PCR analysis or microarray, or at protein expression level by immunohistochemistry or Western blotting when the appropriate antibodies are available. These types of analyses are informative with regard to the altered expression of a specific gene, but do not reveal the net balance between positive and negative cell cycle regulators required to control cell cycle progression.
The cell cycle is divided into 4 phases (G1, S, G2, and M), as shown in FIG. 2A. Cell cycle progression is a multi-step process, and each step is controlled by the distinct balance, in molar ratios, between positive regulators (cyclin dependent kinases (CDK or cdk) and cyclins) and negative regulators (cyclin dependent kinase inhibitors (CDKI)). The mammalian cell cycle is controlled by cytokines and growth factors predominantly at the G1 phase of the cell cycle, the first growth phase following cell division or cell cycle entry. G1 can be subdivided into two phases, the early and late G1 phases, by the mid-G1 restriction point, characterized by phosphorylation of the retinoblastoma (Rb) protein by CDK4 or CDK6 on specific serine sites (called pS-Rb). Rb is the prototype tumor suppressor protein and is a major target for mutations in cancer (Sherr C J, “Cancer Cell Cycles,” Science 274:1672-1677 (1996)). Rb is inactivated in most tumors, either by mutation of the Rb gene or by hyperphosphorylation of the protein as a result of other events in the Rb pathway (Sherr C J, “Cancer Cell Cycles,” Science 274:1672-1677 (1996)). The controls of early and late G1 are distinct. To prepare for cell division, a cell must synthesize new DNA in the “S” phase of the cell cycle, which can be marked by the uptake of nucleotide analogues such as BrdU (5′-deoxybromouridine). Thus, analysis of G1 progression before S phase entry at the single cell level by immunohistochemistry represents an effective means of assessing G1 cell cycle progression, and, therefore, the expansion of cancer cells and disease progression.
It was previously established that one specific INK4 family of the CDK inhibitors, p18INK4c, is required for the generation of normal, functional plasma cells (Morse et al., “Induction of Cell Cycle Arrest and B Cell Terminal Differentiation by CDK Inhibitor p18INK4c and IL-6,” Immunity 6:47-56 (1997); Tourigny et al., “CDK Inhibitor p18INK4c is Required for the Generation of Functional Plasma Cells,” Immunity 17:179-189 (2002)), that p18 expression is lost in some but not all myeloma cells, and that p18 deficiency leads to the development of pituitary adenomas in older mice (Franklin et al., “CDK Inhibitors p18INK4c and p27Kip1 Mediate Two Separate Pathways to Collaboratively Suppress Pituitary Tumorigenesis,” Genes Dev 12:2899-2911 (1998)). At least one other INK4 family protein, p16, has also been implicated in myeloma pathogenesis because it is silenced by promoter hyper-methylation. However, there is no direct evidence that loss of a single INK inhibitor is sufficient to cause cancer. Overexpression of positive cell cycle regulators such as cyclin D1 or D3 has also been implicated in myeloma pathogenesis. However, the expression of cyclin D1 or D3 in myeloma cells bears no relationship to the cell cycle status. Together, these findings reinforce the critical importance of determining the balance, and not the expression of a single cell cycle regulator, as an indicator of cell cycle progression in cancers such as myeloma.
Control of cell cycle re-entry and the progression from G1 to S phase regulates physiologic responses, and loss of this control can be critical for oncogenesis (Sherr et al., “Living With or Without Cyclins and Cyclin-Dependent Kinases,” Genes Dev 18:2699-711 (2004)).
D cyclins are essential for development beyond the early embryonic stage and for the hematopoietic lineage, including B-lymphocytes (Kozar et al., “Mouse Development and Cell Proliferation in the Absence of D-Cyclins,” Cell 118:477-91 (2004)). Cyclin D2, in particular, is expressed in B cells, the precursors of antibody secreting plasma cells (PCs), and is needed for cell cycle activation in response to physiologic signals (Tanguay et al., “Regulation of the Catalytic Subunit (p34PSK-J3/cdk4) for the Major D-Type Cyclin in Mature B Lymphocytes,” J Immunol 156:539-48 (1996); Solvason et al., “Cyclin D2 is Essential for BCR-Mediated Proliferation and CD5 B Cell Development,” Int Immunol 12:631-8 (2000)). Following activation by antigen, inhibition of Cdk6 by the early G1 CdkI, p18INK4c (Guan et al., “Growth Suppression by p18, a p16INK4/MTS1- and p14INK4B/MTS2-Related CDK6 Inhibitor, Correlates with Wild-Type pRb Function,” Genes Dev 8:2939-52 (1994); Hirai et al., “Novel INK4 Proteins, p19 and p18, are Specific Inhibitors of the Cyclin D-Dependent Kinases CDK4 and CDK6,” Mal Cell Biol 15:2672-81 (1995)), is specifically required for G1 cell cycle arrest and terminal differentiation to PCs (Morse et al., “Induction of Cell Cycle Arrest and B Cell Terminal Differentiation by CDK Inhibitor p18(INK4c) and IL-6,” Immunity 6:47-56 (1997); Tourigny et al., “CDK Inhibitor p18(INK4c) is Required for the Generation of Functional Plasma Cells,” Immunity 17:179-89 (2002)). Thus, B cell development and function requires specific D cyclins, Cdk4/6, and CdkIs, implying that perturbation of this balance is likely to underlie oncogenesis in the B lineage.
Plasma cells (PCs) are terminally differentiated B cells that secrete immunoglobulin (Ig). Multiple myeloma (MM) is a cancer of plasmacytoid cells that are arrested from terminal differentiation. MM cells reside in the bone marrow (BM) and secrete a monoclonal Ig detected in the peripheral blood. Morbidity and mortality associated with MM are due to bone lysis and fractures, renal failure, and infections. MM is uniformly fatal with an average survival of 3 years, a statistic that has not changed in the past 40 years (Survey Epidemiology and End Results Data. National Cancer Institute website.) Moreover, MM is becoming more prevalent. It is now the second most common blood cancer and the third fastest growing of all cancers in patients 65 and under. It is twice as common in African Americans as in Caucasians.
MM is diagnosed by a clinical scoring system based on blood tests, radiological studies and finding a minimum number of PCs in a liquid marrow aspirate (International Myeloma Working Group, “Criteria for the Classification of Monoclonal Gammopathies, Multiple Myeloma and Related Disorders: A Report of the International Myeloma Working Group,” Br J Haematol 121(5):749-57 (2003)). Although a BM tissue core biopsy (BMBX) is routinely taken along with the aspirate, BMBX findings are not part of any diagnostic or staging system (Ely S A, “Diagnosis and Management of Multiple Myeloma,” Br J Haematol 118(4):1194-5 (2002)). However, studies show that biopsy criteria can provide powerful prognostic information in MM (Sailer et al., “Prognostic Relevance of a Histologic Classification System Applied in Bone Marrow Biopsies from Patients with Multiple Myeloma: a Histopathological Evaluation of Biopsies from 153 Untreated Patients,” Eur J Haematol 54(3):137-46 (1995)). Chromosomal changes in large cohorts correlate statistically with prognosis, but the associations are inconsistent and not explained by biologic data. Patients are staged by peripheral blood biomarkers. Treatment is based only on staging and overall health (Richardson et al., “A Phase 2 Study of Bortezomib in Relapsed, Refractory Myeloma,” N Engl J Med 348(26):2609-17 (2003); International Myeloma Working Group, “Criteria for the Classification of Monoclonal Gammopathies, Multiple Myeloma and Related Disorders: A Report of the International Myeloma Working Group,” Br J Haematol 121(5):749-57 (2003); Weber et al., “Confirmation of Prognostic Value of Model Using B2-Microglobulin (B2M) and Albumin for Multiple Myeloma (MM) Proposed by the International Myeloma Working Group (IMWG) with Similar Results Using B2M Alone,” Abstract #3491 Blood 102(11) (2003); Greipp et al., “A New International Staging System (ISS) for Multiple Myeloma (MM) from the International Myeloma Working Group,” Abstract #664 Blood 102 (11) (2003)).
The importance of dividing patients based on biologic features is underscored by the success of the plasma cell labeling index (PCLI) (Greipp et al., “Clinical, Morphological, and Cell Kinetic Differences Among Multiple Myeloma, Monoclonal Gammopathy of Undetermined Significance, and Smoldering Multiple Myeloma,” Blood 62(1):166-71 (1983)). The PCLI is an ex vivo assay using BrdU incorporation in live PCs from BM aspirates to compute the percentage in S phase of the cell cycle (CC). Prior to the present invention, it was the most powerful prognosticator in MM. However, the method is costly, labor intensive, and requires specialized equipment not available in most hospitals. Also, because it requires live PCs, it can only be performed immediately after an aspirate is drawn. Thus, 20 years after being published, the PCLI only is used in a few research centers. However, PCLI data shows that CC status may be the key biologic feature that differentiates patients.
Although several promising drugs are available, they are utilized based on clinical discretion alone. Aside from the expanded arsenal of drugs and additional blood tests that have improved staging criteria, this system has changed little since the mid-1970s (Greipp et al., “A New International Staging System (ISS) for Multiple Myeloma (MM) from the International Myeloma Working Group,” Abstract #664 Blood 102 (11) (2003)). The current standard of care for some cancers, including, but not limited to non-solid cancers such as multiple myeloma, is to provide no treatment when the patient is in plateau phase, and wait for a relapse to treat the patient.
In the past few years, significant advances have been made in caring for patients with multiple myeloma (MM), the second most common hematopoietic cancer. For the first time since the early 1960s, promising new drugs are available (Richardson et al., “A Phase 2 Study of Bortezomib in Relapsed, Refractory Myeloma,” N Engl J Med 348(26):2609-17 (2003)). However, for optimistic expectations to come to fruition, advances in drug development must be paralleled by advances in how patients are evaluated. The current practice of lumping patients together based only on the extent of disease (stage) rather than separating them into subgroups based on biologic differences belies the heterogeneity of diseases under the MM rubric. Systems for dividing patients based on biopsy findings have lead to increased longevity for patients with Hodgkin lymphoma, testicular, and other common cancers (see “Survey Epidemiology and End Results Data” available on the National Cancer Institute website). To make a similar impact on the survival of patients with MM, a system for predicting which patients will benefit from what therapy must be found. Understanding such cell cycle deregulation will allow for more specific diagnosis of disease state, and afford an opportunity to intervene with better therapeutic approaches designed to treat cancer at different stages of the disease condition.
The present invention is directed to overcoming these and other deficiencies in the art.